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Application of an emotional neural network to facial recognition

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Abstract

In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human cognition, which is the emotional factor. It may currently sound unthinkable to have emotional machines; however, it is possible to simulate certain artificial emotions with the aim of improving machine learning. This paper investigates the efficiency of an emotional neural network, which uses a modified back propagation learning algorithm. The modifed algorithm, namely the emotional BP learning algorithm, has two emotional parameters, anxiety and confidence, that are modeled during machine learning and decision making. The emotional neural network will be implemented to a facial recognition problem using images of faces with different orientations and contrast levels, and its performance will be compared to that of a conventional neural network. Experimental results suggest that artificial emotions can be successfully modeled and efficiciently implemented to improve neural networks learning and generaliztion.

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References

  1. Levine DS (2007) Neural network modeling of emotion. Phys Life Rev 4:37–63. doi:10.1016/j.plrev.2006.10.001

    Article  Google Scholar 

  2. Lewin DI (2001) Why is that computer laughing? IEEE Intell Syst 16(5):79–81

    Google Scholar 

  3. Picard RW (1997) Affective computing. MIT Press, Cambridge

    Google Scholar 

  4. Yoon J, Park S, Kim J (2004) Emotional robotics based on iT_Media. In: Proceedings of the 30th annual conference of the IEEE Industrial Electronics Society, Busan, Korea, November 2–6, 2004, pp 3148–3153

  5. Boehner K, DePaula R, Dourish P, Sengers P (2007) How emotion is made and measured. Int J Hum Comput Stud 65:275–291. doi:10.1016/j.ijhcs.2006.11.016

    Article  Google Scholar 

  6. Anderson ML (2005) Why is AI so scary? Artif Intell 169:201–208. doi:10.1016/j.artint.2005.10.008

    Article  Google Scholar 

  7. Bates J (1994) The role of emotion in believable agents. Commun ACM 37(7):122–125. doi:10.1145/176789.176803

    Article  Google Scholar 

  8. Perlis D (2005) Review of “Hawkins on intelligence: fascination and frustration” by Jeff Hawkins and Sandra Blakeslee, Times Books, 2004. Artif Intell 169:184–191. doi:10.1016/j.artint.2005.10.012

    Article  MathSciNet  Google Scholar 

  9. Cooke DE (2007) Examining artificial and human intelligence. IEEE Intell Syst 22(2):93. doi:10.1109/MIS.2007.23

    Article  MathSciNet  Google Scholar 

  10. Martinez-Miranda J, Aldea A (2005) Emotions in human and artificial intelligence. Comput Hum Behav 21:323–341. doi:10.1016/j.chb.2004.02.010

    Article  Google Scholar 

  11. Ortony A, Clore GL, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, Cambridge

    Google Scholar 

  12. Ushida H, Hirayama Y, Nakajima H (1998) Emotion model for life-like agent and its evaluation. In: Proceedings of the 15th national/10th conference on Artificial Intelligence/Innovative applications of artificial intelligence, Madison, July 1998. American Association for Artificial Intelligence, Menlo Park, pp 62–69

  13. El-Nasr MS, Yen J, Ioerger TR (2000) FLAME—fuzzy logic adaptive model of emotions. Auton Agent Multi Agent Syst 3:219–257

    Article  Google Scholar 

  14. Gratch J (2000) Modeling the interplay between emotion and decision making. In: Proceedings of the ninth conference on computer generated forces and behavioral representation, Orlando, May 2000

  15. Kort B, Reilly R, Picard RW (2001) An affective model of interplay between emotions and learning: reengineering educational pedagogy—building learn companion. In: IEEE international conference on advanced learning technologies (ICALT 2001), Madison, 6–8 August 2001, pp 43–48

  16. Poel M, den Akker R, Nijholt A, van Kesteren AJ (2002) Learning emotions in virtual environments. In: Trappl R (ed) Proceedings of the sixteenth European meeting on cybernetics and system research (Vienna, 2–5 April 2002), vol. 2, pp 751–756

  17. Doya K (2002) Meta-learning and neuro-modulation. Neural Netw 15(4–6):495–506

    Article  Google Scholar 

  18. Clocksin WF (2004) Memory and emotion in the cognitive architecture. In: Davis D (ed) Visions of mind. IDEA Group Publishing, Hershey, pp 122–139

    Google Scholar 

  19. Rousseau D, Hayes-Roth B (1997) Improvisational synthetic actors with flexible personalities. Research report KSL 97-10. Knowledge Systems Laboratory, Stanford University, Stanford

  20. Canamero D (2003) Designing emotions for activity selection in autonomous agents. In: Trappl R, Petta P, Payr S (eds) Emotions in humans and artifacts. MIT Press, Cambridge

    Google Scholar 

  21. Abu Maria K, Abu Zitar R (2007) Emotional agents: a modeling and an application. Inf Softw Technol 49:695–716. doi:10.1016/j.infsof.2006.08.002

    Article  Google Scholar 

  22. Gobbini MI, Haxby JV (2007) Neural systems for recognition of familiar faces. Neuropsychologia 45:32–41. doi:10.1016/j.neuropsychologia.2006.04.015

    Article  Google Scholar 

  23. Khashman A (2008) A modified back propagation learning algorithm with added emotional coefficients. IEEE Trans Neural Netw 19(11):1896–1909

    Article  Google Scholar 

  24. Khashman A (2006) Face recognition using neural networks and pattern averaging. Lect Notes Comput Sci 3972:98–103

    Article  Google Scholar 

  25. Khashman A (2007) Intelligent global face recognition. In: Delac K, Grgic M (eds) Face recognition. ITECH Education and Publishing, Vienna, Chapter 11, ISBN 978-3-902613-03-5

  26. Rumelhart D, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart D, McClelland J (eds) Parallel distributed processing, vol 1. MIT Press, Cambridge

    Google Scholar 

  27. Baumgartner T, Esslen M, Jancke L (2006) From emotion perception to emotion experience: emotions evoked by pictures and classical music. Int J Psychophysiol 60:34–43. doi:10.1016/j.ijpsycho.2005.04.007

    Article  Google Scholar 

  28. Khashman A (2006) Intelligent face recognition: local versus global pattern averaging. Lect Notes Artif Intell 4304:956–961

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Intelligent Systems Research Group (ISRG), Near East University, Lefkosa, Mersin 10, Turkey

    Adnan Khashman

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  1. Adnan Khashman
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Correspondence to Adnan Khashman.

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Khashman, A. Application of an emotional neural network to facial recognition. Neural Comput & Applic 18, 309–320 (2009). https://doi.org/10.1007/s00521-008-0212-4

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  • DOI: https://doi.org/10.1007/s00521-008-0212-4

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